NEURAL SENTINAL -ADAPTIVE AI FOR REAL-TIME INTRUSION PREVENTION

Unauthorized access to sensitive areas is a critical security issue in various sectors, including government, military, and private properties. This paper presents a computer vision-based surveillance system designed to prevent unauthorized access in real-time. Leveraging advanced techniques such as object detection, facial recognition, and motion analysis, the proposed system continuously monitors restricted zones, automatically detecting and responding to intrusions. The solution improves on traditional surveillance by offering real- time alerts, automated monitoring, and reduced human intervention. By utilizing machine learning models, the system adapts to new security threats, ensuring robustness across varied environments. This scalable solution aims to enhance security by integrating seamlessly into existing infrastructures. Keywords: Computer vision, restricted area prevention, object detection, real-time monitoring, automated surveillance, facial recognition, anomaly detection.